crseEvent: Clustering robust t-statistics for abnormal returns in...

In crseEventStudy: A Robust and Powerful Test of Abnormal Stock Returns in Long-Horizon Event Studies

Clustering robust t-statistics for abnormal returns in long-horizon event studies

Description

crseEvent implements a robust statistical test developed by Dutta et al. (JempFin, 2018).

The test is based on abnormal standardized returns and offers three implementations. Standardized returns are defined as sr_{it} = \frac{r_{it}}{s_{it}} where s_{it} is a standard deviation estimator of log returns r_{it}:

Use of Abnormal standardized returns (ASR)

Abnormal standardized returns are defined as ASR_{it} = sr_{it} - sr_{ci,t}, where sr_{ci,t} is the standardized return of the matching control firm or the average of standardized returns of the matching control portfolio.

Use of Standardized abnormal returns (SAR)

Standardized abnormal returns are defined as SAR_{it} = \frac{r_{event} - r_{control}}{sd_{event-control}}. The matching control return should be derived from a single firm observation and not be the return-series of a portfolio.

Use of Continuously compounded abnormal returns (CCAR)

Continuously compounded abnormal returns are defined as CCAR_{it} = r_{it} - r_{ci,t}, where r_{it} = log(1 + R_{it}) is the event month t continuously compounded return (i.e., log-return) of event stock i, and r_{ci,t} is the continuously compounded return of the control firm.

Usage

crseEvent(data, abnr = "ars", cluster1 = "yyyymm",
  cluster2 = NULL, na.rm = TRUE, na.replace = 0)

Arguments

data	an object of class "data.frame" (or one that can be coerced to that class)
abnr	Name of a column from data which contains abnormal standardized returns (ASR), standardized abnormal returns (SAR) or continuously compounded abnormal returns (CCAR).
cluster1	Name of a column from data which contains the primarely cluster-variables for the observations.
cluster2	Name of a column from data which contains additional cluster-variables for the observations.
na.rm	An object of class "logical": If na.rm is TRUE, missing observations for the item abnr will be removed from the dataset. Otherwise missing oberservations will be replaced with a value defined in na.replace
na.replace	A numeric scalar: If data contains missing observations for the abnormal return series and na.rm is FALSE, these missing return values will be overritten with the value of na.replace

Value

crseEvent returns an object of class crse and list.

The returning value of "crseEvent" is a "list" containing the following components:

N	Total number of observations.
mean.abnormal.ret	Mean abnormal return.
t.val.nonclustered	Non-clustered (common) t-value.
p.val.nonclustered	Non-clustered (common) p-value.
t.val.one.clustered	One-way clustered t-value.
p.val.one.clustered	One-way clustered p-value.
tcl2	One-way clustering t-value with respect to second clustering variable (NA if cluster2 is NULL).
pcl2	One-way clustering p-value with respect to second clustering variable (NA if cluster2 is NULL).
tcl12	2-way clustering t-value (NA if cluster2 is NULL).
pcl12	2-way clustering p-value (NA if cluster2 is NULL).
cluster1	Name of the first cluster variable.
cluster2	Name of the second cluster variable.
reg.fit	Regression results on which t-value compuations are based.
var.cl1	Robust variance of abnormal return series with regard to one-way clustering on variable cluster1.
var.cl2	Robust variance of abnormal return series with regard to one-way clustering on variable cluster2.
var.cl12	Robust variance of abnormal return series with regard to two-way clustering on both variable cluster1 and cluster2.
unique.cl1	Total number of unique observations by clustering on variable cluster1.
unique.cl2	Total number of unique observations by clustering on variable cluster2.

References

Dutta, A., Knif, J., Kolari, J.W., Pynnonen, S. (2018): A robust and powerful test of abnormal stock returns in long-horizon event studies. Journal of Empirical Finance, 47, p. 1-24. doi: 10.1016/j.jempfin.2018.02.004.

Examples

## load demo_share_repurchases
## one-way clustering on column "date" and print summary statistics
data(demo_share_repurchases)
crse <- crseEvent(demo_share_repurchases, abnr="ars", cluster1 = "date")
summary(crse)

## print mean of abnormal return series
crse$mean.abnormal.ret

crseEventStudy documentation built on March 18, 2022, 7:20 p.m.